The present invention relates to the art of diagnostic imaging of small pulmonary nodules. In particular, the present invention is related to analyzing and manipulating computed tomography scans to: segment the lungs, measure lung volume, locate and determine the size of the nodules without explicit segmentation, register the nodules using a rigid-body transformation, and removing the pleural surface from juxtapleural nodules in thresholded images.
Lung cancer is the leading cause of cancer deaths among the population in the United States. Each year there are about 170,000 newly diagnosed cases of lung cancer and over 150,000 deaths. More people die of lung cancer than of colon, breast, and prostate cancers combined. Despite the research and improvements in medical treatments related to surgery, radiation therapy, and chemotherapy, currently the overall survival rate of all lung cancer patients is only about 14 percent. Unfortunately the survival rate has remained essentially the same over the past three decades. The high mortality rate of lung cancer is caused by the fact that more than 80% lung cancer is diagnosed after it has metastasized. Patients with early detection of lung cancer followed by proper treatment with surgery and/or combined with radiation and chemotherapy can improve their five-year survival rate from 13 percent to about 41 percent. Given that earlier-stage intervention leads to substantially higher rates of survival, it is therefore a major public health directive to reduce the mortality of lung cancer through detection and intervention of the cancer at earlier and more curable stages.
The development of the computed tomography (CT) technology and post-processing algorithms has provided radiologists with a useful tool for diagnosing lung cancers at early stages. However, current CT systems have their inherent shortcomings in that the amount of chest CT images (data) that is generated from a single CT examination, which can range from 30 to over 300 slices depending on image resolution along the scan axial direction, becomes a huge hurdle for the radiologists to interpret. Accordingly, there is a constant need for the improvement and development of diagnostic tools for enabling a radiologist to review and interpret the vast amount of information that is obtained through a CT examination.
International Publication No. WO 01/78005 A2 discloses a system and method for three dimensional image rendering and analysis, and is incorporated herein by reference. The system performs a variety of tasks that aid a radiologist in interpreting the results of a CT examination.
One task that radiologists focus on is segmenting the lung region from the image of a single slice obtained from the CT examination. In the prior art, some have suggested using a linear discriminant function and morphological filtering to automatically segment the lungs (S. Hu, E. A. Hoffman, and J. M. Reinhardt, “Automatic Lung Segmentation for Accurate Quantitation of Volumetric X-Ray CT Images,” IEEE Transactions on Medical Imaging, Vol 20, No 6, June 2001, which is incorporated herein by reference) while others have used mean and median filtering to remove the streaking artifacts due to excessive x-ray quantum noise (J. Hsieh, “Generalized Adaptive Median Filters and their Application in CT,” SPIE, Vol 2299, 1994; J. Hsieh, “Adaptive Trimmed Mean Filter for CT Imaging,” SPIE, Vol 2299, 1994 which is incorporated herein by reference).
Radiologists also study the location and size of the pulmonary nodules in the CT scan. It is preferred if the radiologist could perform this analysis without the use of explicit segmentation. In some prior work, the location of a nodule was determined by finding the center of mass of the nodule through an iterative correlation-based procedure (A. P. Reeves, W. J. Kostis, D. F. Yankelevitz, C. I. Henschke, “Analysis of Small Pulmonary Nodules without Explicit Segmentation of CT images,” Radiological Society of North America—2000 Scientific Program, vol. 217, pgs. 243-4, November 2000 which is incorporated herein by reference). The method works for isolated pulmonary nodules, but fails on nodules attached to the pleural surface.
Radiologists also estimate a measurement of doubling time of a nodule by registering two separate images of the nodule taken at two different times (time-1 and time-2). This analysis requires that the time-1 and time-2 nodules be registered correctly so that the growth can be properly measured. Other objects such as vessels and bronchial tubes must also be registered together. This results in their absence in the difference image and little effect on the growth measurement. Previously, two nodules were registered by finding the centers of mass of the nodules and translating the image accordingly (A. P. Reeves, W. J. Kostis, D. F. Yankelevitz, C. I. Henschke, “Analysis of Small Pulmonary Nodules without Explicit Segmentation of CT images,” Radiological Society of North America—2000 Scientific Program, vol. 217, pgs. 243-4, November 2000 which is incorporated herein by reference). However, this analysis did not guarantee that the two nodules would be correctly orientated, and that the other objects in the image would registered because these objects might be rotated about the nodule. Some have registered the nodules by performing a maximization search of the mutual information metric over the rigid-body transformation parameters (F. Maes, A. Collignon, D. Vandermeulen, G. Marchal and P. Suetens, “Multimodality image registration by maximization of mutual information,” IEEE Transactions on Medical Imaging, vol. 16, no. 2, pgs. 187-198, April 1997; Takagi, N.; Kawata, Y.; Nikvi, N.; Morit, K; Ohmatsu, H.; Kakinuma, R.; Eguchi, K.; Kusumoto, M.; Kaneko, M.; Moriyama, N. “Computerized characterization of contrast enhancement patterns for classifying pulmonary nodules” Image Processing, 2000. Proceedings. 2000 International Conference on, vol. 1, pgs. 188-191, 2000 which are incorporated herein by reference).
Radiologists also need to remove the pleural surface from juxtapleural nodules in CT images. In some prior work, three-dimensional morphological filtering and mathematical moments were used to segment a juxtapleural nodule from pleural surface in a binary image (A. P. Reeves, W. J. Kostis, “Computer-Aided Diagnosis of Small Pulmonary Nodules,” Seminars in Ultrasound, CT, and MRI, vol. 21, no. 2, pgs. 116-128, April 2000 which is incorporated herein by reference).